20 Best Books For Data Analytics (2023 Update)

Make better decisions with this easy deconstructed guide to data analytics. want to add data analytics to your skill stack? having trouble finding where to start? cell by cell, bit by bit, this book teaches you the vocabulary, tools, and basic algorithms to think like a data scientist. like assembling a complex lego set, each chapter connects and adds individual blocks of knowledge to build your data literacy. this linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems like a data scientist. – who is this book for? this book is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. if you've tried to learn data analytics before and failed, this book is for you. – a practical approach – this book takes a hands-on approach to learning. this includes practical examples, visual examples, as well as two bonus coding exercises in python, including free video content to walk you through both exercises. by the end of the book, you will have the practical knowledge to tackle real data problems in your organization or daily life. – what you will learn from reading this book- how to recognize the common data types every data scientist needs to master- where to store your data, including big data – new trends in data analytics, including what is alternative data and why not many people know about it- how to explain the distinction between data mining, machine learning, and analytics to your colleagues- when and how to use regression analysis, classification, clustering, association analysis, and natural language processing- how to make better business decisions using data visualization and business intelligence

Learn how to use r to turn raw data into insight, knowledge, and understanding. this book introduces you to r, rstudio, and the tidyverse, a collection of r packages designed to work together to make data science fast, fluent, and fun. suitable for readers with no previous programming experience, r for data science is designed to get you doing data science as quickly as possible. – authors hadley wickham and garrett grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. you'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. each section of the book is paired with exercises to help you practice what you've learned along the way. – you'll learn how to: wrangle–transform your datasets into a form convenient for analysis – program–learn powerful r tools for solving data problems with greater clarity and ease – explore–examine your data, generate hypotheses, and quickly test them – model–provide a low-dimensional summary that captures true "signals" in your dataset – communicate–learn r markdown for integrating prose, code, and results

The importance of data analytics is well known, but how can you get end users to engage with analytics and business intelligence (bi) when adoption of new technology can be frustratingly slow or may not happen at all? avoid wasting time on dashboards and reports that no one uses with this practical guide to increasing analytics adoption by focusing on people and process, not technology. pulling together agile, ux and change management principles, delivering data analytics outlines a step-by-step, technology agnostic process designed to shift the organizational data culture and gain buy-in from users and stakeholders at every stage of the project. this book outlines how to succeed and build trust with stakeholders amid the politics, ambiguity and lack of engagement in business. with case studies, templates, checklists and scripts based on the author's considerable experience in analytics and data visualisation, this book covers the full cycle from requirements gathering and data assessment to training and launch. ensure lasting adoption, trust and, most importantly, actionable business value with this roadmap to creating user-centric analytics projects.

Adeptly address today’s business challenges with this powerful new book from web analytics thought leader avinash kaushik. web analytics 2.0 presents a new framework that will permanently change how you think about analytics. the book will help your organization become more data driven while you become a super analysis ninja! note: cd-rom/dvd and other supplementary materials are not included as part of ebook file.

Take your first steps to become a fully qualified data analyst by learning how to explore large relational datasets key features explore a variety of statistical techniques to analyze your data integrate your sql pipelines with other analytics technologies perform advanced analytics such as geospatial and text analysis book description understanding and finding patterns in data has become one of the most important ways to improve business decisions. if you know the basics of sql, but don't know how to use it to gain the most effective business insights from data, this book is for you. sql for data analytics helps you build the skills to move beyond basic sql and instead learn to spot patterns and explain the logic hidden in data. you'll discover how to explore and understand data by identifying trends and unlocking deeper insights. you'll also gain experience working with different types of data in sql, including time-series, geospatial, and text data. finally, you'll learn how to increase your productivity with the help of profiling and automation. by the end of this book, you'll be able to use sql in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the github repository. the link to the github repository can be found in the book's preface. what you will learn perform advanced statistical calculations using the window function use sql queries and subqueries to prepare data for analysis import and export data using a text file and psql apply special sql clauses and functions to generate descriptive statistics analyze special data types in sql, including geospatial data and time data optimize queries to improve their performance for faster results debug queries that won't run use sql to summarize and identify patterns in data who this book is for if you're a database engineer looking to transition into analytics, or a backend engineer who wants to develop a deeper understanding of production data, you will find this book useful. this book is also ideal for data scientists or business analysts who want to improve their data analytics skills using sql. knowledge of basic sql and database concepts will aid in understanding the concepts covered in this book.

Don't simply show your data—tell a story with it! storytelling with data teaches you the fundamentals of data visualization and how to communicate effectively with data. you'll discover the power of storytelling and the way to make data a pivotal point in your story. the lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. this book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. specifically, you'll learn how to: understand the importance of context and audience determine the appropriate type of graph for your situation recognize and eliminate the clutter clouding your information direct your audience's attention to the most important parts of your data think like a designer and utilize concepts of design in data visualization leverage the power of storytelling to help your message resonate with your audience together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. rid your world of ineffective graphs, one exploding 3d pie chart at a time. there is a story in your data—storytelling with data will give you the skills and power to tell it!

This book is a comprehensive introduction to the methods and algorithms of modern data analytics. it provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. this book has been used for more than ten years in the data mining course at the technical university of munich. much of the content is based on the results of industrial research and development projects at siemens. – content – data analytics – data and relations – data preprocessing – data visualization – correlation – regression – forecasting – classification – clustering – target groups – students of computer science, mathematics and engineering – data analytics practitioners – the author runkler is principal research scientist at siemens corporate technology and professor for computer science at the technical university of munich.

Key features: learn the art of telling stories with your data – get ahead in your career by thinking like a data scientist – start working with data to get better insights – book description: data analytics made easy is an accessible guide for anyone new to working with data. it focuses on how to generate insights from your data at a click of a button using popular tools, without having to write a line of code! the book helps you start analyzing data and quickly apply these skills to your work. – data analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. if you want to make sense of your data and add value with informed decisions, this is the book for you. – the book introduces the concepts of data analytics and shows you how to get your data ready and apply algorithms. implement analytics solutions to predict future trends and assess their levels of accuracy. create impressive visualizations and learn the greatest secret in successful analytics – how to tell a story with your data. you'll connect the dots on the various stages of the data-to-insights process. – what you will learn: understand the potential of data and its impact on any business – influence business decisions with effective data storytelling when delivering insights – import, clean, transform, combine data feeds, and automate your processes – learn the basics of machine learning to add value to your organization – create professional-looking and business-centric visuals and dashboards – who this book is for: if you are new to working with data and you want to progress in your career, you'll find this book an effective way to add analytics to your skill stack. – no previous math, statistics, or computer science knowledge is required.

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming a general introduction to data analytics is an essential guide to understand and use data analytics. this book is written using easy-to-understand terms and does not require familiarity with statistics or programming. the authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. the text also contains exercises and illustrative examples. thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. it explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. the book also explores predictive tasks, be them classification or regression. finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. the learning resources offer: a guide to the reasoning behind data mining techniques a unique illustrative example that extends throughout all the chapters exercises at the end of each chapter and larger projects at the end of parts ii and iii of the book supplemented with powerpoint slides available for instructors on a wiley book companion site together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. the book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. a general introduction to data analytics is a basic guide to data analytics written in highly accessible terms.

Process tabular data and build high-performance query engines on modern cpus and gpus using apache arrow, a standardized language-independent memory format, for optimal performance – key features: learn about apache arrow's data types and interoperability with pandas and parquet – work with apache arrow flight rpc, compute, and dataset apis to produce and consume tabular data – reviewed, contributed, and supported by dremio, the co-creator of apache arrow – book description: apache arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily. – in-memory analytics with apache arrow begins with a quick overview of the apache arrow format, before moving on to helping you to understand arrow's versatility and benefits as you walk through a variety of real-world use cases. you'll cover key tasks such as enhancing data science workflows with arrow, using arrow and apache parquet with apache spark and jupyter for better performance and hassle-free data translation, as well as working with perspective, an open source interactive graphical and tabular analysis tool for browsers. as you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between arrow, parquet, feather, protobuf, flatbuffers, json, and csv. in addition to understanding the basic structure of the arrow flight and flight sql protocols, you'll learn about dremio's usage of apache arrow to enhance sql analytics and discover how arrow can be used in web-based browser apps. finally, you'll get to grips with the upcoming features of arrow to help you stay ahead of the curve. – by the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with apache arrow. – what you will learn: use apache arrow libraries to access data files both locally and in the cloud – understand the zero-copy elements of the apache arrow format – improve read performance by memory-mapping files with apache arrow – produce or consume apache arrow data efficiently using a c apiuse the apache arrow compute apis to perform complex operations – create arrow flight servers and clients for transferring data quickly – build the arrow libraries locally and contribute back to the communitywho this book is for: this book is for developers, data analysts, and data scientists looking to explore the capabilities of apache arrow from the ground up. this book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. code examples are provided in the c++, go, and python programming languages.

It doesn't matter if your business has three employees or three hundred, you are likely generating far more information that you may realize, and certainly far more than you are likely tracking effectively. understanding what this data truly means starts with managing it successfully which is where the process of data analytics comes into play. if you like the sound of putting your data to good use, but aren't quite sure what the ins and outs of data analytics entails then data analytics: 4 books in 1- bible of 4 manuscipts- beginner's guide+ tips and tricks+ effective strategies+ best practices to learn data analytics efficiently, will be your perfect learning guide. on average, there are roughly two quintillion bytes worth of new data created each and every day which means that knowing what to do with it is easily a full-time job. luckily, there are a wide variety of options out there when it comes to focusing in on the data that you want to use and using it in the most effective way possible. inside you will find all the tools you are going to need in order to do just that, regardless if you are part of multinational conglomerate or are running your own startup. so, what are you waiting for?

For many organizations data is a by-product, but for the smarter ones it is the heartbeat of their business. beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity. it takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors. data and analytics strategy for business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, ai and machine learning can improve performance and decision making. filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.

This edition presents a broad-based survey of business forecasting methods, including subjective and objective approaches. the authors, keating and wilson, deliver practical how-to forecasting techniques, along with dozens of real-world data sets while holding theory and math to a minimum. – today, most business planning routinely begins with a sales forecast. whether you are an accountant, a marketer, a human resources manager, a data scientist, or a financial analyst, sooner or later, you will have to predict something. this book is designed to lead students through the most helpful techniques to use in any prediction effort. – new to this edition – four new chapters focus on predictive analytics, recognizing the importance of these tools in today's prediction efforts. – examples of predictive analytics are based on historical data, much like what students may encounter in their own forecasts. – techniques are explained as procedures, allowing students to replicate them with their own data. – all chapters now include learning objectives. – the forecast – x software sections are updated and clarified. – retained features – real-world data are used throughout the text, which consists almost entirely of real-world examples, showing readers how to deal with real-life problems and difficulties in a realistic environment. – extended examples are presented in each chapter. two examples are used to demonstrate the progression of techniques needed to solve each issue; these examples provide a realistic illustration of the concepts that are relevant to the student used in the text. one example features sales data from the gap, whereas another example uses domestic car sales data. – by consistently providing all the data in excel, the leading calculation software used in business, students recognize its compatibility with forecast – x software.

Do you want to expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data centric issues? exploring this book could be a step in the right direction… 2 comprehensive manuscripts in 1 book data science: what the best data scientists know about data analytics, data mining, statistics, machine learning, and big data – that you don't data science for business: predictive modeling, data mining, data analytics, data warehousing, data visualization, regression analysis, database querying, and machine learning for beginners part one of this book will cover topics such as: what data science is what it takes to become an expert in data science best data mining techniques to apply in data data visualization logistic regression data engineering machine learning big data analytics and much more part 2 of this book will discuss the following topics: how big data works and why it is so important how to do an explorative data analysis working with data mining how to mine text to get the data some amazing machine learning algorithms to help with data science how to do data modeling data visualization how to use data science to help your business grow tips to help you get started with data science and much, much more so if you want to learn more about data science, click "buy now"

The text seamlessly threads the topics of data wrangling, descriptive analytics, predictive analytics, and prescriptive analytics into a cohesive whole. these revisions are based on the feedback of reviewers and users of our first edition. the greatly expanded coverage of the text gives instructors the flexibility to select the topics that best align with their course objectives

In this rich, fascinating — surprisingly accessible — introduction, leading expert eric siegel reveals how predictive analytics works, and how it affects everyone every day. rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. prediction is booming. it reinvents industries and runs the world. companies, governments, law enforcement, hospitals, and universities are seizing upon the power. these institutions predict whether you're going to click, buy, lie, or die. why? for good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. how? prediction is powered by the world's most potent, flourishing unnatural resource: data. accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. surprise! this heap of refuse is a gold mine. big data embodies an extraordinary wealth of experience from which to learn. predictive analytics unleashes the power of data. with this technology, the computer literally learns from data how to predict the future behavior of individuals. perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. in this lucid, captivating introduction — now in its revised and updated edition — former columbia university professor and predictive analytics world founder eric siegel reveals the power and perils of prediction: what type of mortgage risk chase bank predicted before the recession. predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. five reasons why organizations predict death — including one health insurance company. how u.s. bank and obama for america calculated — and hillary for america 2016 plans to calculate — the way to most strongly persuade each individual. why the nsa wants all your data: machine learning supercomputers to fight terrorism. how ibm's watson computer used predictive modeling to answer questions and beat the human champs on tv's jeopardy! how companies ascertain untold, private truths — how target figures out you're pregnant and hewlett-packard deduces you're about to quit your job. how does predictive analytics work? this jam-packed book satisfies by demystifying the intriguing science under the hood. for future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. a truly omnipresent science, predictive analytics constantly affects our daily lives. whether you are a consumer of it — or consumed by it — get a handle on the power of predictive analytics.

In this rich, fascinating — surprisingly accessible — introduction, leading expert eric siegel reveals how predictive analytics works, and how it affects everyone every day. rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. prediction is booming. it reinvents industries and runs the world. companies, governments, law enforcement, hospitals, and universities are seizing upon the power. these institutions predict whether you're going to click, buy, lie, or die. why? for good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. how? prediction is powered by the world's most potent, flourishing unnatural resource: data. accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. surprise! this heap of refuse is a gold mine. big data embodies an extraordinary wealth of experience from which to learn. predictive analytics unleashes the power of data. with this technology, the computer literally learns from data how to predict the future behavior of individuals. perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. in this lucid, captivating introduction — now in its revised and updated edition — former columbia university professor and predictive analytics world founder eric siegel reveals the power and perils of prediction: what type of mortgage risk chase bank predicted before the recession. predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. five reasons why organizations predict death — including one health insurance company. how u.s. bank and obama for america calculated — and hillary for america 2016 plans to calculate — the way to most strongly persuade each individual. why the nsa wants all your data: machine learning supercomputers to fight terrorism. how ibm's watson computer used predictive modeling to answer questions and beat the human champs on tv's jeopardy! how companies ascertain untold, private truths — how target figures out you're pregnant and hewlett-packard deduces you're about to quit your job. how does predictive analytics work? this jam-packed book satisfies by demystifying the intriguing science under the hood. for future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. a truly omnipresent science, predictive analytics constantly affects our daily lives. whether you are a consumer of it — or consumed by it — get a handle on the power of predictive analytics.

Written by renowned data science experts foster provost and tom fawcett, data science for business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. this guide also helps you understand the many data-mining techniques in use today. – based on an mba course provost has taught at new york university over the past ten years, data science for business provides examples of real-world business problems to illustrate these principles. you’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. you’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. – understand how data science fits in your organization—and how you can use it for competitive advantage – treat data as a business asset that requires careful investment if you’re to gain real value – approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way – learn general concepts for actually extracting knowledge from data – apply data science principles when interviewing data science job candidates

Today, the world is trying to create and educate data scientists because of the phenomenon of big data. and everyone is looking deeply into this technology. but no one is looking at the larger architectural picture of how big data needs to fit within the existing systems (data warehousing systems). taking a look at the larger picture into which big data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. most references on big data look at only one tiny part of a much larger whole. until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. data architecture a primer for the data scientist addresses the larger architectural picture of how big data fits with the existing information infrastructure, an essential topic for the data scientist. drawing upon years of practical experience and using numerous examples and an easy to understand framework. inmon, and daniel linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. you’ll be able to: turn textual information into a form that can be analyzed by standard tools. – make the connection between analytics and big data – understand how big data fits within an existing systems environment conduct analytics on repetitive and non-repetitive data – discusses the value in big data that is often overlooked, non-repetitive data, and why there is significant business value in using it – shows how to turn textual information into a form that can be analyzed by standard tools – explains how big data fits within an existing systems environment presents new opportunities that are afforded by the advent of big data demystifies the murky waters of repetitive and non-repetitive data in big data

During the past decade there has been an explosion in computation and information technology. with it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. the challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. many of these tools have common underpinnings but are often expressed with different terminology. this book describes the important ideas in these areas in a common conceptual framework. while the approach is statistical, the emphasis is on concepts rather than mathematics. many examples are given, with a liberal use of color graphics. it is a valuable resource for statisticians and anyone interested in data mining in science or industry. the book's coverage is broad, from supervised learning (prediction) to unsupervised learning. the many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. this major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. there is also a chapter on methods for “wide'' data (p bigger than n), including multiple testing and false discovery rates. they are prominent researchers in this area: hastie and tibshirani developed generalized additive models and wrote a popular book of that title. hastie co-developed much of the statistical modeling software and environment in r/s-plus and invented principal curves and surfaces. tibshirani proposed the lasso and is co-author of the very successful an introduction to the bootstrap. friedman is the co-inventor of many data-mining tools including cart, mars, projection pursuit and gradient boosting.